An inverse association between self-reported allergic conditions (ACs) and glioma has been previously observed in seven case-control and two cohort studies. The mechanism for this association is not known, however, two cytokines that play a central role in ACs (i.e., interleukin (IL)-4, IL-13) also suppress glioma growth. From among genetic polymorphisms that are associated with risk of ACs, we have identified polymorphisms also involved in normal astrocyte growth or glioma pathogenesis. Gene products of genetic polymorphisms found on the IL-4, IL-13, IL-4Ralpha, IL-13Ralpha1, HLA -DRB1, RANTES, and neuronal nitric oxide synthase (nNOS) play a role in both ACs and the brain. We will examine the case-control distribution of these polymorphisms and haplotypes to: 1) test the hypothesis that polymorphisms associated with ACs reduce glioma risk; 2) identify differential misclassification of serf-reported ACs (unequal measurement of cases and controls); 3) find out whether antihistamine use interferes with protection against glioma afforded by ACs; and 4) determine whether AC polymorphisms affect glioma risk independently of their association with ACs. The innovation in the proposed study is to focus on polymorphisms associated with ACs, an epidemiologic risk factor that reduces glioma risk, rather than on polymorphisms associated with carcinogenesis as has been done in previous studies. Our pilot study will make use of interviews with 260 glioma cases and 450 controls that have already been conducted and blood samples that have been collected in conjunction a population-based case-control study of mobile telephone use and brain tumors in Sweden. Study participants were asked whether they have been diagnosed with asthma, eczema, hay fever or other allergies, continue to have allergies, have symptoms of allergies without a diagnosis, and have used allergy medication. Twenty ml of blood from each participant was collected and stored in a -80 degree freezer and DNA preparation is ongoing. Each participant will be evaluated for the presence AC polymorphisms. Unconditional logistic regression will be used to estimate the association between AC polymorphisms and glioma risk. Assuming a 0.05 significance level and 80 percent power, depending on the polymorphism, the proposed sample is large enough to detect a confounding-adjusted odds ratios of 0.6.